ashraf kassim

Singapore University of Technology and Design (SUTD), Information Systems Technology and Design (ISTD), Faculty Member

National University of Singapore, Electrical & Computer Engineering, Faculty Member

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Judith L Green

University of California, Santa Barbara

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Antioch University

E. Wayne Ross

University of British Columbia

George K. # Zarifis

Aristotle University of Thessaloniki

Andreas Fejes

Linköping University

John Field

University of Stirling

Dawn Bennett

Bond University

Oludeyi Olukunle Saheed

Tai Solarin University of Education

Anna D. Rowe

The University of New South Wales

Alexander McCormick

Indiana University

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Papers by ashraf kassim

Extracting 3D models of objects using modified silhouette based volume intersection

IEEE International Conference on Multimedia and Expo, 2001. ICME 2001.

In this paper, we present a object modeling technique based on volume interaction using silhouett... more In this paper, we present a object modeling technique based on volume interaction using silhouettes of objects placed far away from the camera. The method is flexible and easy to use. By using a generic calibration matrix, we surpass the stringent requirements of accurate camera calibration inherent in volume intersection algorithms. This widens the scope of utility of this technique even to the laymen without significant back ground in computer vision. In addition, we propose a modified intersection test to determine inclusion/ exclusion of projected cubes which is fast, accurate and does not require any additional sophisticated hardware. We successfully conducted experimental test on synthetic objects and real images taken through video input. The technique is highly suitable for modeling objects for virtual environments, Internet shopping, e-commerce, etc. and requires easily available, Ubiquitous hardware.

Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification

Lecture Notes in Computer Science, 2022

Deep learning approaches achieve state-of-the-art performance for classifying radiology images, b... more Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by specialists. Both semi-supervised learning and active learning can be utilised to mitigate this annotation burden. However, there is limited work on combining the advantages of semi-supervised and active learning approaches for multi-label medical image classification. Here, we introduce a novel Consistencybased Semi-supervised Evidential Active Learning framework (CSEAL). Specifically, we leverage predictive uncertainty based on theories of evidence and subjective logic to develop an end-to-end integrated approach that combines consistency-based semi-supervised learning with uncertainty-based active learning. We apply our approach to enhance four leading consistency-based semi-supervised learning methods: Pseudo-labelling, Virtual Adversarial Training, Mean Teacher and NoTeacher. Extensive evaluations on multi-label Chest X-Ray classification tasks demonstrate that CSEAL achieves substantive performance improvements over two leading semi-supervised active learning baselines. Further, a class-wise breakdown of results shows that our approach can substantially improve accuracy on rarer abnormalities with fewer labelled samples.

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